Explainable Multi-task Learning Approach for Skin Lesion Classification

Patel, K; Mehta, N; Easwaran, S; Walambe, R; Gite, S; Pradhan, B; Saini, B

Explainable Multi-task Learning Approach for Skin Lesion Classification

Patel, K Mehta, N Easwaran, S Walambe, R Gite, S Pradhan, B

Saini, B

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Publisher:: Springer Nature
Publication Type:: Chapter
Citation:: Smart Sensors, Measurement and Instrumentation, 2024, 50, pp. 279-300
Issue Date:: 2024-01-01

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Field	Value	Language
dc.contributor.author	Patel, K
dc.contributor.author	Mehta, N
dc.contributor.author	Easwaran, S
dc.contributor.author	Walambe, R
dc.contributor.author	Gite, S
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054
dc.contributor.author	Saini, B
dc.date.accessioned	2025-01-23T05:01:51Z
dc.date.available	2025-01-23T05:01:51Z
dc.date.issued	2024-01-01
dc.identifier.citation	Smart Sensors, Measurement and Instrumentation, 2024, 50, pp. 279-300
dc.identifier.isbn	9783031686016
dc.identifier.uri	http://hdl.handle.net/10453/184087
dc.description.abstract	The early diagnosis of skin cancer has significantly improved with the use of computer-aided techniques and deep learning (DL) models. However, existing methods often struggle with issues of interpretability and adaptability, which are crucial for clinical application. To address these limitations, we employed a Multi-Task Learning (MTL) approach that simultaneously performs classification and segmentation of skin lesions. This approach not only improves the accuracy and robustness of the models but also enhances their interpretability by incorporating Explainable AI (XAI) techniques into the MTL framework. Our convolutional-deconvolutional based MTL model, tested on the HAM-10000 dataset, demonstrated enhanced classification accuracy and interpretability with an Accuracy of 91.56% and IoU Score of 87.98%. The model outperformed state-of-the-art models, showing a marked enhancement in classification accuracy. Importantly, the use of MTL facilitates a reduction in model complexity while achieving superior performance, making this approach both powerful and efficient. From a practical standpoint, the replicability of our MTL framework is a key advantage, providing a scalable model for researchers and clinicians. The methodology’s adaptability to different imaging datasets underscores its potential utility across various dermatological conditions. Future research could leverage this framework to further refine diagnostic accuracy in other complex imaging tasks, enhancing the scope of AI in medical diagnostics.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Smart Sensors, Measurement and Instrumentation
dc.relation.ispartofseries	Smart Sensors, Measurement and Instrumentation
dc.relation.isbasedon	10.1007/978-3-031-68602-3_14
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Explainable Multi-task Learning Approach for Skin Lesion Classification
dc.type	Chapter
utslib.citation.volume	50
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Transport Research Centre (TRC)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Centre for Advanced Modelling and Geospatial lnformation Systems (CAMGIS)
pubs.organisational-group	University of Technology Sydney/UTS Groups/Transport Research Centre (TRC)/Transport Research Centre (TRC) Associate Members
utslib.copyright.status	closed_access	*
dc.date.updated	2025-01-23T05:01:50Z
pubs.publication-status	Published
pubs.volume	50

Abstract:

The early diagnosis of skin cancer has significantly improved with the use of computer-aided techniques and deep learning (DL) models. However, existing methods often struggle with issues of interpretability and adaptability, which are crucial for clinical application. To address these limitations, we employed a Multi-Task Learning (MTL) approach that simultaneously performs classification and segmentation of skin lesions. This approach not only improves the accuracy and robustness of the models but also enhances their interpretability by incorporating Explainable AI (XAI) techniques into the MTL framework. Our convolutional-deconvolutional based MTL model, tested on the HAM-10000 dataset, demonstrated enhanced classification accuracy and interpretability with an Accuracy of 91.56% and IoU Score of 87.98%. The model outperformed state-of-the-art models, showing a marked enhancement in classification accuracy. Importantly, the use of MTL facilitates a reduction in model complexity while achieving superior performance, making this approach both powerful and efficient. From a practical standpoint, the replicability of our MTL framework is a key advantage, providing a scalable model for researchers and clinicians. The methodology’s adaptability to different imaging datasets underscores its potential utility across various dermatological conditions. Future research could leverage this framework to further refine diagnostic accuracy in other complex imaging tasks, enhancing the scope of AI in medical diagnostics.

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http://hdl.handle.net/10453/184087